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Sample Data
Version 2.0.0 offers CT, MR, PET, NIfTI, and NRRD samples plus a matched CT/PET pair, downloaded from Apple’s servers and restorable from Settings. They are sourced from publicly available, de-identified research datasets and contain no personally identifiable information. Each dataset's source, license, and citation are detailed below.
CT Sample — LIDC-IDRI CC BY 3.0
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., … Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, … Croft BY. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915–931, 2011. DOI: https://doi.org/10.1118/1.3528204
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
MR Sample — UPENN-GBM CC BY 4.0
Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49
Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., … Davatzikos, C. (2022). The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. In Scientific Data (Vol. 9, Issue 1). https://doi.org/10.1038/s41597-022-01560-7
Reported research was partly supported by the National Cancer Institute (NCI), the National Institute of Neurological Disorders and Stroke (NINDS), and the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under award numbers NINDS:R01NS042645, NCI:U24CA189523, NCI:U01CA242871, NCATS:UL1TR001878, and by the Institute for Translational Medicine and Therapeutics (ITMAT) of the University of Pennsylvania. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH, or the ITMAT of the UPenn.
PET Sample — ACRIN-NSCLC-FDG-PET CC BY 3.0
Kinahan, P., Muzi, M., Bialecki, B., Herman, B., & Coombs, L. (2019). Data from the ACRIN 6668 Trial NSCLC-FDG-PET (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.30ilqfcl
Machtay, M., Duan, F., Siegel, B. A., Snyder, B. S., Gorelick, J. J., Reddin, J. S., … & Alavi, A. (2013). Prediction of Survival by [18F]Fluorodeoxyglucose Positron Emission Tomography in Patients With Locally Advanced Non–Small-Cell Lung Cancer Undergoing Definitive Chemoradiation Therapy: Results of the ACRIN 6668/RTOG 0235 Trial. In Journal of Clinical Oncology (Vol. 31, Issue 30, pp. 3823–3830). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2012.47.5947
This shared data set was provided in collaboration with the American College of Radiology Core Lab. Many thanks are due to the ACRIN 6668 trial team, and all the patients participating in the study. This study was supported by the American College of Radiology Imaging network (ACRIN), which received funding from the National Cancer Institute through UO1 CA080098, U01 CA21661, U01 CA32115, U01 CA190254, and R50 CA211270 (Muzi), under the American Recovery and Reinvestment ACT of 2009 (ARRA) and UO1 CA079778. Please see QIN ECOG-ACRIN Data Sharing page for an overview and list of other ECOG-ACRIN data collections available on TCIA.
CT + PET Pair Sample — Soft-tissue-Sarcoma CC BY 3.0
Vallières, Martin, Freeman, Carolyn R., Skamene, Sonia R., & El Naqa, Issam. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities (Soft-tissue-Sarcoma) [Dataset]. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.7GO2GSKS
Vallières, M., Freeman, C. R., Skamene, S. R., & Naqa, I. El. (2015, June 29). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology. IOP Publishing. http://doi.org/10.1088/0031-9155/60/14/5471
TCIA Platform Citation (applies to the TCIA datasets on this page)
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7
NRRD Sample — 3D Slicer MRHead donated for unrestricted use
MRHead sample dataset, 3D Slicer SampleData. Donated to the 3D Slicer project by the individuals visible in the images, for use without restriction. https://www.slicer.org/
Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. (2012). 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging, 30(9), 1323–1341. https://doi.org/10.1016/j.mri.2012.05.001
The MRHead dataset is provided through the 3D Slicer SampleData module and is used here with thanks to the 3D Slicer project and its contributors.
NIfTI Sample — OpenNeuro ds000030 CC0 1.0
Bilder, R., Poldrack, R., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., & Sabb, F. (2020). UCLA Consortium for Neuropsychiatric Phenomics LA5c Study [Data set]. OpenNeuro. https://doi.org/10.18112/openneuro.ds000030.v1.0.0
Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. Scientific Data, 3, 160110. https://doi.org/10.1038/sdata.2016.110
This data was obtained from the OpenfMRI/OpenNeuro database (accession number ds000030). This work was supported by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410).
Validation References
Reference phantoms and datasets used in the VoxelVision 1.2.1 geometric-validation study (manuscript in peer review). Analysis code, measurement records, and per-tier execution lists are available at github.com/mingweicui/VV-measurement-validation (MIT License).
CatPhan504 CTP404 module — ideal-cue reference phantom
The Phantom Laboratory, Incorporated. (2025). Catphan® 504 manual — product guide [Product guide]. The Phantom Laboratory. https://www.phantomlab.com/catphan-504
The external-reference scan of the CTP404 module is obtained from the publicly available pylinac demonstration dataset. Kerns, J. R. (2023). Pylinac: Image analysis for routine quality assurance in radiotherapy. Journal of Open Source Software, 8(92), 6001. https://doi.org/10.21105/joss.06001
MCVV Author-built phantom datasets CC BY 4.0
Cui, M. (2026). MCVV phantom datasets (MCVV-SPH, MCVV-RPH): Synthetic and anatomy-like phantoms with geometric ground truth for volumetric medical-image measurement [Data set]. Zenodo. https://doi.org/10.5281/zenodo.21029537
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